similar to: spdep question - Moran's I

Dear all, I have a question regarding the spdep package: Assume that each person within my dataset is characterized by a continuous variable y. In order to test for spatial autocorrelation in y I calculated a Moran's I statistic. The problem is that y is also likely to be influenced by a series of other variables x that are unique to each person in my dataset. To test to which extent the

I want to do "moran's I test" in R language. I try to use "gearymoran" in Package "ade4","moran" in Package "spdep", and Moran.I in Package "ape". But I do not know how to do it because data format is different. My data: x y dbh 111.03 10.7 7 118.11 0.28 1.2 165.36 0.36 8.4

Dear , This is Elaine. I am computing moran's I using moran.test for a generalized linear model (multiregression). The following contents are the results, and I cannot find the observed Moran's I mentioned as estimate in the manual. Please kindly help indicate if there is observed Moran's I did not notice or other method for calculation. Thanks Elaine data: residuals(modg1)

Dear all, I have a large dataset (N=100,000 with 89 variables per subject). This dataset is stored in a 100.000 x 89 matrix where each row describes one individual and each column one variable. What is the easiest way of selecting a subset of let's say 1.000 individuals out of that whole matrix? Thanks, Michael Michael Haenlein Associate Professor of Marketing ESCP-EAP European School of

Dear All, We aim to remove the spatial structure of our data using Moran Eigen Vectors and spdep package . Our data has 3694 samples and 13 variables. The computer stop working after almost 4 days of processing (we found it emitting a sharp sound and with all colors on the screen. No wories, it was restared without problem!). And we are left with nothing: no result file was produced since the

Hello everybody, I would like to use the SpDep-package (especially the Local Moran index analysis and the Getis-Ord statistics) in R for analysing my data. However, I don't have x-y coordinates, but my data is in a distance matrix format. Is it possible to use the SpDep package with predefined distances as well instead of letting the program determining the distance itself by the function

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Hi, I am trying to calculate Moran's I test for the residuals for a regression equation, but I have trouble converting my coordinates into nb format. I have used the dnearneigh() funtion now with an arbitrarily high upper distance to make it include all plots. However, when I do the lm.morantest() I get a Moran's I value which is the same as the expected value and a P-value of 1. I

My question is in regard to the spdep and GeoDa packages. Do you know how to extract a neighborhood/adjacency matrix from R? I need a matrix that has all the neighborhoods on the X and Y axis, and then a r=1 when the neighborhoods touch, and a r=0 when neighborhoods don't touch. I think the Moran's I functions use a matrix like this when they estimate Moran's I. I can estimate

Hi, I am looking for some code for Moran's I. Has anyone previously done this? I have been unable to find it in the search engines. James

i would like to perform moran's analysis with r... thanks duccio

Hello- I am using the vegan package PCMN() because I need to use option of row weights (I am using CCA following the selection of the significant/positive PCNM variables). In the library PCNM (PCNM function), the Moran's I is calculated for you as part of the function. I see that I need to calculate Moran's I separately in order to retain the eigenfunctions with positive spatial

The attachment is my dataset: 1.ccc.shp (the original data) 2.ccc.gwt, which is computed by Geoda; Introduction to the variables in my data: ID: key variable; N_LATITUDE: latitude measured by GPS; E_LONGITUD: longitude measured by GPS; LIVES: attribute data I get the different result of Moran's between ARCGIS and Geoda, R, why? ARCGIS:spatial statistics tools:spatial

Hello List, I'm a very new user to the R system. I'm only beginning to learn the basics, but so far I've been able to do little more than try a few examples, and of course begin reading the documentation. My primary motivation for exploring R is the availability of tools like the 'spdep' package for calculating spatial statistics such as Geary's C and Moran's

Dear R-users, I was wondering if someone could give me some advices on the following problem. I tried to apply moran’s test to a small dataset and couldn’t succeed, here is the error message:   mor <- moran.test(x, res2)   Avis dans moran.test(x, res2) :   Out-of-range p-value: reconsider test arguments   mor     Moran's I test under randomization data:  x  weights: res2 

dear R users, I have troubles performing Moran.I test as suggested on http://www.ats.ucla.edu/stat/r/faq/morans_i.htm my spatial data are longitude and lattitide of communities. The calculation of the inverse distance matrix according to the homepage (using my data) datAL <- read.csv2("C:\\Konvergenz AL.csv", header=T) ALdist <- as.matrix(dist(cbind(datAL$L?nge,

Dear all, I'm running a coxph model of the form: coxph(Surv(Start, End, Death.ID) ~ x1 + x2 + a1 + a2 + a3) Within this model, I would like to compare the influence of x1 and x2 on the hazard rate. Specifically I am interested in testing whether the estimated coefficient for x1 is equal (or not) to the estimated coefficient for x2. I was thinking of using a Chow-test for this but the Chow

Dear R users, I've written a loop to generate Moran's test (spdep package) on serval subsamples of a large dataset. See below a short example. My loop is working fine, however I would like to be able to store the test results as lines of a matrix, that I would latter be able to export as a dataset. My problem is that I'm not sure how I could do this using R. Any help will be much

Dear all, I'm struggling with predicting "expected time until death" for a coxph and survreg model. I have two datasets. Dataset 1 includes a certain number of people for which I know a vector of covariates (age, gender, etc.) and their event times (i.e., I know whether they have died and when if death occurred prior to the end of the observation period). Dataset 2 includes another